Overview

Dataset statistics

Number of variables13
Number of observations162
Missing cells162
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory104.8 B

Variable types

Numeric12
Categorical1

Warnings

Country has a high cardinality: 162 distinct values High cardinality
Confirmed is highly correlated with Deaths and 2 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 1 other fieldsHigh correlation
Recovered is highly correlated with New_cases and 3 other fieldsHigh correlation
Active is highly correlated with Confirmed and 1 other fieldsHigh correlation
New_cases is highly correlated with Confirmed and 5 other fieldsHigh correlation
New_deaths is highly correlated with New_cases and 1 other fieldsHigh correlation
New_recovered is highly correlated with Recovered and 4 other fieldsHigh correlation
Total_Recovered is highly correlated with Recovered and 3 other fieldsHigh correlation
Recovered_Percent is highly correlated with Recovered and 3 other fieldsHigh correlation
Confirmed is highly correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Active is highly correlated with Confirmed and 7 other fieldsHigh correlation
New_cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
New_deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
New_recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Total_Recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Recovered_Percent is highly correlated with Confirmed and 7 other fieldsHigh correlation
Confirmed is highly correlated with Deaths and 6 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 6 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 5 other fieldsHigh correlation
Active is highly correlated with Confirmed and 6 other fieldsHigh correlation
New_cases is highly correlated with Confirmed and 4 other fieldsHigh correlation
New_deaths is highly correlated with Confirmed and 4 other fieldsHigh correlation
New_recovered is highly correlated with Recovered and 4 other fieldsHigh correlation
Total_Recovered is highly correlated with Confirmed and 5 other fieldsHigh correlation
Recovered_Percent is highly correlated with Confirmed and 5 other fieldsHigh correlation
New_recovered is highly correlated with New_cases and 5 other fieldsHigh correlation
New_cases is highly correlated with New_recovered and 7 other fieldsHigh correlation
New_deaths is highly correlated with New_recovered and 6 other fieldsHigh correlation
Confirmed is highly correlated with New_recovered and 7 other fieldsHigh correlation
Recovered is highly correlated with New_recovered and 5 other fieldsHigh correlation
Population_(in_thousands)_total is highly correlated with New_casesHigh correlation
Total_Recovered is highly correlated with New_recovered and 5 other fieldsHigh correlation
Active is highly correlated with New_cases and 3 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 1 other fieldsHigh correlation
Recovered_Percent is highly correlated with New_recovered and 5 other fieldsHigh correlation
Confirmed has 18 (11.1%) missing values Missing
Deaths has 18 (11.1%) missing values Missing
Recovered has 18 (11.1%) missing values Missing
Active has 18 (11.1%) missing values Missing
New_cases has 18 (11.1%) missing values Missing
New_deaths has 18 (11.1%) missing values Missing
New_recovered has 18 (11.1%) missing values Missing
Total_Recovered has 18 (11.1%) missing values Missing
Recovered_Percent has 18 (11.1%) missing values Missing
Country is uniformly distributed Uniform
df_index has unique values Unique
Country has unique values Unique
Population_annual_growth_rate_(%) has 2 (1.2%) zeros Zeros
Deaths has 16 (9.9%) zeros Zeros
Recovered has 5 (3.1%) zeros Zeros
Active has 4 (2.5%) zeros Zeros
New_cases has 29 (17.9%) zeros Zeros
New_deaths has 80 (49.4%) zeros Zeros
New_recovered has 56 (34.6%) zeros Zeros
Total_Recovered has 5 (3.1%) zeros Zeros
Recovered_Percent has 5 (3.1%) zeros Zeros

Reproduction

Analysis started2022-01-27 09:05:17.244459
Analysis finished2022-01-27 09:05:33.517941
Duration16.27 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.25925926
Minimum0
Maximum184
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:33.602944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.05
Q144.25
median94.5
Q3136.75
95-th percentile173.95
Maximum184
Range184
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation53.93400102
Coefficient of variation (CV)0.5909975762
Kurtosis-1.228570574
Mean91.25925926
Median Absolute Deviation (MAD)46.5
Skewness-0.01065380737
Sum14784
Variance2908.876467
MonotonicityStrictly increasing
2022-01-27T14:35:33.711834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1841
 
0.6%
441
 
0.6%
641
 
0.6%
631
 
0.6%
621
 
0.6%
601
 
0.6%
591
 
0.6%
581
 
0.6%
571
 
0.6%
561
 
0.6%
Other values (152)152
93.8%
ValueCountFrequency (%)
01
0.6%
11
0.6%
21
0.6%
31
0.6%
41
0.6%
51
0.6%
61
0.6%
71
0.6%
91
0.6%
101
0.6%
ValueCountFrequency (%)
1841
0.6%
1831
0.6%
1821
0.6%
1811
0.6%
1801
0.6%
1781
0.6%
1771
0.6%
1751
0.6%
1741
0.6%
1731
0.6%

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Dominican Republic
 
1
Mauritius
 
1
Mali
 
1
Fiji
 
1
Sweden
 
1
Other values (157)
157 

Length

Max length32
Median length7.5
Mean length9.24691358
Min length4

Characters and Unicode

Total characters1498
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Dominican Republic1
 
0.6%
Mauritius1
 
0.6%
Mali1
 
0.6%
Fiji1
 
0.6%
Sweden1
 
0.6%
Guatemala1
 
0.6%
Portugal1
 
0.6%
Iceland1
 
0.6%
Azerbaijan1
 
0.6%
Algeria1
 
0.6%
Other values (152)152
93.8%

Length

2022-01-27T14:35:33.940141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and6
 
2.7%
republic4
 
1.8%
saint3
 
1.4%
islands3
 
1.4%
guinea3
 
1.4%
rep3
 
1.4%
new3
 
1.4%
congo2
 
0.9%
netherlands2
 
0.9%
china2
 
0.9%
Other values (187)188
85.8%

Most occurring characters

ValueCountFrequency (%)
a223
14.9%
i128
 
8.5%
n113
 
7.5%
e109
 
7.3%
o89
 
5.9%
r84
 
5.6%
l57
 
3.8%
57
 
3.8%
u53
 
3.5%
t48
 
3.2%
Other values (45)537
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1212
80.9%
Uppercase Letter213
 
14.2%
Space Separator57
 
3.8%
Other Punctuation12
 
0.8%
Dash Punctuation2
 
0.1%
Open Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a223
18.4%
i128
10.6%
n113
9.3%
e109
9.0%
o89
 
7.3%
r84
 
6.9%
l57
 
4.7%
u53
 
4.4%
t48
 
4.0%
s47
 
3.9%
Other values (16)261
21.5%
Uppercase Letter
ValueCountFrequency (%)
S24
11.3%
C20
 
9.4%
B19
 
8.9%
M19
 
8.9%
N14
 
6.6%
A13
 
6.1%
G13
 
6.1%
P12
 
5.6%
L12
 
5.6%
R11
 
5.2%
Other values (12)56
26.3%
Other Punctuation
ValueCountFrequency (%)
,5
41.7%
.5
41.7%
'2
 
16.7%
Space Separator
ValueCountFrequency (%)
57
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1425
95.1%
Common73
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a223
15.6%
i128
 
9.0%
n113
 
7.9%
e109
 
7.6%
o89
 
6.2%
r84
 
5.9%
l57
 
4.0%
u53
 
3.7%
t48
 
3.4%
s47
 
3.3%
Other values (38)474
33.3%
Common
ValueCountFrequency (%)
57
78.1%
,5
 
6.8%
.5
 
6.8%
'2
 
2.7%
-2
 
2.7%
(1
 
1.4%
)1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a223
14.9%
i128
 
8.5%
n113
 
7.5%
e109
 
7.3%
o89
 
5.9%
r84
 
5.6%
l57
 
3.8%
57
 
3.8%
u53
 
3.5%
t48
 
3.2%
Other values (45)537
35.8%

Population_(in_thousands)_total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct155
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12181.80387
Minimum2
Maximum160943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:34.040472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile68.3
Q1913
median5409
Q312661.46108
95-th percentile39442.75
Maximum160943
Range160941
Interquartile range (IQR)11748.46108

Descriptive statistics

Standard deviation23688.70406
Coefficient of variation (CV)1.944597393
Kurtosis23.36785329
Mean12181.80387
Median Absolute Deviation (MAD)4901.5
Skewness4.508260728
Sum1973452.228
Variance561154699.9
MonotonicityNot monotonic
2022-01-27T14:35:34.137473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12661.461087
 
4.3%
7392
 
1.2%
191591
 
0.6%
13281
 
0.6%
1001
 
0.6%
57431
 
0.6%
66401
 
0.6%
69691
 
0.6%
4551
 
0.6%
52591
 
0.6%
Other values (145)145
89.5%
ValueCountFrequency (%)
21
0.6%
101
0.6%
141
0.6%
201
0.6%
311
0.6%
331
0.6%
501
0.6%
581
0.6%
681
0.6%
741
0.6%
ValueCountFrequency (%)
1609431
0.6%
1559911
0.6%
1447201
0.6%
862641
0.6%
810211
0.6%
606441
0.6%
483791
0.6%
455581
0.6%
394591
0.6%
391341
0.6%

Population_annual_growth_rate_(%)
Real number (ℝ)

ZEROS

Distinct50
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.347164929
Minimum-2.5
Maximum4.3
Zeros2
Zeros (%)1.2%
Negative16
Negative (%)9.9%
Memory size1.4 KiB
2022-01-27T14:35:34.250399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile-0.495
Q10.5
median1.377245509
Q32.1
95-th percentile3.1
Maximum4.3
Range6.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.179776256
Coefficient of variation (CV)0.8757474529
Kurtosis0.3310562222
Mean1.347164929
Median Absolute Deviation (MAD)0.822754491
Skewness-0.1632871861
Sum218.2407186
Variance1.391872013
MonotonicityNot monotonic
2022-01-27T14:35:34.346125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3772455097
 
4.3%
0.57
 
4.3%
0.37
 
4.3%
1.17
 
4.3%
1.77
 
4.3%
2.56
 
3.7%
0.66
 
3.7%
1.86
 
3.7%
1.35
 
3.1%
2.25
 
3.1%
Other values (40)99
61.1%
ValueCountFrequency (%)
-2.51
 
0.6%
-2.21
 
0.6%
-1.12
1.2%
-0.91
 
0.6%
-0.71
 
0.6%
-0.61
 
0.6%
-0.52
1.2%
-0.41
 
0.6%
-0.34
2.5%
-0.12
1.2%
ValueCountFrequency (%)
4.31
 
0.6%
41
 
0.6%
3.92
1.2%
3.61
 
0.6%
3.51
 
0.6%
3.31
 
0.6%
3.21
 
0.6%
3.13
1.9%
34
2.5%
2.91
 
0.6%

Confirmed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct142
Distinct (%)98.6%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean16136.09722
Minimum10
Maximum301708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:34.459378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile24.45
Q1824.5
median3133.5
Q315701.5
95-th percentile66995.8
Maximum301708
Range301698
Interquartile range (IQR)14877

Descriptive statistics

Standard deviation33081.59998
Coefficient of variation (CV)2.050161172
Kurtosis38.91159733
Mean16136.09722
Median Absolute Deviation (MAD)3047.5
Skewness5.17137023
Sum2323598
Variance1094392258
MonotonicityNot monotonic
2022-01-27T14:35:34.558751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
862
 
1.2%
106212
 
1.2%
793951
 
0.6%
1141
 
0.6%
641561
 
0.6%
5091
 
0.6%
4621
 
0.6%
6741
 
0.6%
114241
 
0.6%
241
 
0.6%
Other values (132)132
81.5%
(Missing)18
 
11.1%
ValueCountFrequency (%)
101
0.6%
121
0.6%
141
0.6%
171
0.6%
181
0.6%
201
0.6%
231
0.6%
241
0.6%
271
0.6%
481
0.6%
ValueCountFrequency (%)
3017081
0.6%
1164581
0.6%
924821
0.6%
820401
0.6%
811611
0.6%
793951
0.6%
711811
0.6%
670961
0.6%
664281
0.6%
643791
0.6%

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct111
Distinct (%)77.1%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean869.7361111
Minimum0
Maximum45844
Zeros16
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:34.666753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median59.5
Q3333.5
95-th percentile2580.85
Maximum45844
Range45844
Interquartile range (IQR)322.5

Descriptive statistics

Standard deviation4044.85707
Coefficient of variation (CV)4.650671644
Kurtosis108.7461998
Mean869.7361111
Median Absolute Deviation (MAD)58.5
Skewness9.921162681
Sum125242
Variance16360868.71
MonotonicityNot monotonic
2022-01-27T14:35:34.769862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016
 
9.9%
114
 
2.5%
73
 
1.9%
23
 
1.9%
13
 
1.9%
103
 
1.9%
83
 
1.9%
222
 
1.2%
352
 
1.2%
692
 
1.2%
Other values (101)103
63.6%
(Missing)18
 
11.1%
ValueCountFrequency (%)
016
9.9%
13
 
1.9%
23
 
1.9%
31
 
0.6%
41
 
0.6%
51
 
0.6%
61
 
0.6%
73
 
1.9%
83
 
1.9%
103
 
1.9%
ValueCountFrequency (%)
458441
0.6%
98221
0.6%
89441
0.6%
61601
0.6%
57001
0.6%
55321
0.6%
46521
0.6%
26471
0.6%
22061
0.6%
19781
0.6%

Recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct136
Distinct (%)94.4%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean7084.340278
Minimum0
Maximum55057
Zeros5
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:34.867518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.15
Q1279.5
median1548
Q36587.75
95-th percentile34540.7
Maximum55057
Range55057
Interquartile range (IQR)6308.25

Descriptive statistics

Standard deviation11256.0658
Coefficient of variation (CV)1.588865774
Kurtosis3.320876643
Mean7084.340278
Median Absolute Deviation (MAD)1523.5
Skewness1.978748305
Sum1020145
Variance126699017.3
MonotonicityNot monotonic
2022-01-27T14:35:34.959446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
3.1%
182
 
1.2%
1282
 
1.2%
8032
 
1.2%
392
 
1.2%
324551
 
0.6%
221
 
0.6%
328561
 
0.6%
4401
 
0.6%
16161
 
0.6%
Other values (126)126
77.8%
(Missing)18
 
11.1%
ValueCountFrequency (%)
05
3.1%
81
 
0.6%
111
 
0.6%
121
 
0.6%
131
 
0.6%
151
 
0.6%
182
 
1.2%
191
 
0.6%
221
 
0.6%
231
 
0.6%
ValueCountFrequency (%)
550571
0.6%
456921
0.6%
372021
0.6%
361101
0.6%
353751
0.6%
350861
0.6%
348961
0.6%
348381
0.6%
328561
0.6%
324551
0.6%

Active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct132
Distinct (%)91.7%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean8182.020833
Minimum0
Maximum254427
Zeros4
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:35.307158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q190.25
median785
Q34708
95-th percentile40496.15
Maximum254427
Range254427
Interquartile range (IQR)4617.75

Descriptive statistics

Standard deviation25533.98103
Coefficient of variation (CV)3.120742607
Kurtosis62.29309013
Mean8182.020833
Median Absolute Deviation (MAD)776.5
Skewness7.065655334
Sum1178211
Variance651984187.2
MonotonicityNot monotonic
2022-01-27T14:35:35.410148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
2.5%
13
 
1.9%
23
 
1.9%
212
 
1.2%
522
 
1.2%
15992
 
1.2%
92
 
1.2%
132
 
1.2%
4761
 
0.6%
1791
 
0.6%
Other values (122)122
75.3%
(Missing)18
 
11.1%
ValueCountFrequency (%)
04
2.5%
13
1.9%
23
1.9%
41
 
0.6%
81
 
0.6%
92
1.2%
121
 
0.6%
132
1.2%
151
 
0.6%
181
 
0.6%
ValueCountFrequency (%)
2544271
0.6%
1075141
0.6%
736951
0.6%
536491
0.6%
529921
0.6%
470641
0.6%
470561
0.6%
407331
0.6%
391541
0.6%
363781
0.6%

New_cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct88
Distinct (%)61.1%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean179.8680556
Minimum0
Maximum2029
Zeros29
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:35.521219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median24
Q3159
95-th percentile724.55
Maximum2029
Range2029
Interquartile range (IQR)156.25

Descriptive statistics

Standard deviation342.2070335
Coefficient of variation (CV)1.902544799
Kurtosis10.46339334
Mean179.8680556
Median Absolute Deviation (MAD)24
Skewness2.995285886
Sum25901
Variance117105.6538
MonotonicityNot monotonic
2022-01-27T14:35:35.628195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029
 
17.9%
15
 
3.1%
114
 
2.5%
133
 
1.9%
73
 
1.9%
243
 
1.9%
103
 
1.9%
43
 
1.9%
33
 
1.9%
53
 
1.9%
Other values (78)85
52.5%
(Missing)18
 
11.1%
ValueCountFrequency (%)
029
17.9%
15
 
3.1%
22
 
1.2%
33
 
1.9%
43
 
1.9%
53
 
1.9%
62
 
1.2%
73
 
1.9%
81
 
0.6%
91
 
0.6%
ValueCountFrequency (%)
20291
0.6%
17521
0.6%
15921
0.6%
12481
0.6%
11461
0.6%
11041
0.6%
8351
0.6%
7311
0.6%
6881
0.6%
6821
0.6%

New_deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct23
Distinct (%)16.0%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean3.638888889
Minimum0
Maximum64
Zeros80
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:35.730608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile16.85
Maximum64
Range64
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.904003273
Coefficient of variation (CV)2.446901663
Kurtosis22.46227029
Mean3.638888889
Median Absolute Deviation (MAD)0
Skewness4.375050525
Sum524
Variance79.28127428
MonotonicityNot monotonic
2022-01-27T14:35:35.809096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
080
49.4%
114
 
8.6%
29
 
5.6%
37
 
4.3%
45
 
3.1%
65
 
3.1%
54
 
2.5%
113
 
1.9%
132
 
1.2%
72
 
1.2%
Other values (13)13
 
8.0%
(Missing)18
 
11.1%
ValueCountFrequency (%)
080
49.4%
114
 
8.6%
29
 
5.6%
37
 
4.3%
45
 
3.1%
54
 
2.5%
65
 
3.1%
72
 
1.2%
81
 
0.6%
91
 
0.6%
ValueCountFrequency (%)
641
0.6%
501
0.6%
461
0.6%
281
0.6%
271
0.6%
201
0.6%
191
0.6%
171
0.6%
161
0.6%
141
0.6%

New_recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct70
Distinct (%)48.6%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean107.6666667
Minimum0
Maximum1601
Zeros56
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:35.901411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3103.5
95-th percentile672.75
Maximum1601
Range1601
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation233.9982368
Coefficient of variation (CV)2.173358237
Kurtosis14.54854139
Mean107.6666667
Median Absolute Deviation (MAD)5
Skewness3.488900208
Sum15504
Variance54755.17483
MonotonicityNot monotonic
2022-01-27T14:35:35.996411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056
34.6%
25
 
3.1%
44
 
2.5%
14
 
2.5%
63
 
1.9%
392
 
1.2%
152
 
1.2%
1032
 
1.2%
32
 
1.2%
222
 
1.2%
Other values (60)62
38.3%
(Missing)18
 
11.1%
ValueCountFrequency (%)
056
34.6%
14
 
2.5%
25
 
3.1%
32
 
1.2%
44
 
2.5%
52
 
1.2%
63
 
1.9%
81
 
0.6%
112
 
1.2%
141
 
0.6%
ValueCountFrequency (%)
16011
0.6%
10071
0.6%
9551
0.6%
8431
0.6%
8291
0.6%
7491
0.6%
6841
0.6%
6811
0.6%
6261
0.6%
5581
0.6%

Total_Recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct136
Distinct (%)94.4%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean7192.006944
Minimum0
Maximum55741
Zeros5
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:36.110732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.15
Q1281
median1594.5
Q36925.25
95-th percentile34656.3
Maximum55741
Range55741
Interquartile range (IQR)6644.25

Descriptive statistics

Standard deviation11402.35621
Coefficient of variation (CV)1.585420633
Kurtosis3.281412196
Mean7192.006944
Median Absolute Deviation (MAD)1570
Skewness1.971340951
Sum1035649
Variance130013727
MonotonicityNot monotonic
2022-01-27T14:35:36.201132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
3.1%
1282
 
1.2%
182
 
1.2%
392
 
1.2%
8032
 
1.2%
43011
 
0.6%
1271
 
0.6%
111
 
0.6%
1891
 
0.6%
1041
 
0.6%
Other values (126)126
77.8%
(Missing)18
 
11.1%
ValueCountFrequency (%)
05
3.1%
81
 
0.6%
111
 
0.6%
121
 
0.6%
131
 
0.6%
151
 
0.6%
182
 
1.2%
191
 
0.6%
221
 
0.6%
231
 
0.6%
ValueCountFrequency (%)
557411
0.6%
458631
0.6%
375191
0.6%
365311
0.6%
360411
0.6%
358451
0.6%
355331
0.6%
348961
0.6%
332981
0.6%
329591
0.6%

Recovered_Percent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct136
Distinct (%)94.4%
Missing18
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean71.92006944
Minimum0
Maximum557.41
Zeros5
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-01-27T14:35:36.303130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1215
Q12.81
median15.945
Q369.2525
95-th percentile346.563
Maximum557.41
Range557.41
Interquartile range (IQR)66.4425

Descriptive statistics

Standard deviation114.0235621
Coefficient of variation (CV)1.585420633
Kurtosis3.281412196
Mean71.92006944
Median Absolute Deviation (MAD)15.7
Skewness1.971340951
Sum10356.49
Variance13001.3727
MonotonicityNot monotonic
2022-01-27T14:35:36.396530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
3.1%
1.282
 
1.2%
8.032
 
1.2%
0.392
 
1.2%
0.182
 
1.2%
1.891
 
0.6%
0.231
 
0.6%
557.411
 
0.6%
19.241
 
0.6%
1.931
 
0.6%
Other values (126)126
77.8%
(Missing)18
 
11.1%
ValueCountFrequency (%)
05
3.1%
0.081
 
0.6%
0.111
 
0.6%
0.121
 
0.6%
0.131
 
0.6%
0.151
 
0.6%
0.182
 
1.2%
0.191
 
0.6%
0.221
 
0.6%
0.231
 
0.6%
ValueCountFrequency (%)
557.411
0.6%
458.631
0.6%
375.191
0.6%
365.311
0.6%
360.411
0.6%
358.451
0.6%
355.331
0.6%
348.961
0.6%
332.981
0.6%
329.591
0.6%

Interactions

2022-01-27T14:35:19.168080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.272877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.374227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.466211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.562223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.649609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.739359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.824795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:19.916789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.004191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.103081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.192379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.286278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.391646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.501602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.599624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.702492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.799173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:20.892667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.000747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.103211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.366327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.473759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.581887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.679506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.777259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.876184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:21.980438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.070801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.157900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.258784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.351698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.461590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.562146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.656654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.746298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.832299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:22.918298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.014218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.101221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.199520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.283520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.371197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.454369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.550304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.635304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.728756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.818497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:23.914023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.002744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.098746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.200014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.291389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.368452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.448522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.541110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.626442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.708503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.797092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.877241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:24.955707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.043782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.138211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.229517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.319023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.402170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.486864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.571870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.662522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.743363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.839694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:25.929368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.010437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.096437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.194778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.278778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.362778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.445838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.525907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.609349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.695826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.777832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.871824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:26.958976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.043555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.135557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.238134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.340626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.436681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.523489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.617488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.713526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.812723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:27.915013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.204428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.299356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.396791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.487972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.587365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.676781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.770189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.849598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:28.932447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.020075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.117002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.204270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.310902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.395905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.480660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.579326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.687647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.782122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.879966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:29.965966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.055108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.148355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.247891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.341329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.446649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.544051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.637727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.722354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.825526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.909799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:30.999322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.078322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.161322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.243322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.334727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.424360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.516768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.602409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.685836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.775243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.868687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:31.961673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.051814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.132739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.213740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.303823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.393144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.480924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.573564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-27T14:35:32.660479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-01-27T14:35:36.495038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-27T14:35:36.679080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-27T14:35:36.859981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-27T14:35:37.030981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-27T14:35:32.827759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-27T14:35:33.047881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-27T14:35:33.222686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-27T14:35:33.403861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCountryPopulation_(in_thousands)_totalPopulation_annual_growth_rate_(%)ConfirmedDeathsRecoveredActiveNew_casesNew_deathsNew_recoveredTotal_RecoveredRecovered_Percent
00Afghanistan26088.04.036263.01269.025198.09796.0106.010.018.025216.0252.16
11Albania3172.00.64880.0144.02745.01991.0117.06.063.02808.028.08
22Algeria33351.01.527973.01163.018837.07973.0616.08.0749.019586.0195.86
33Andorra74.01.0907.052.0803.052.010.00.00.0803.08.03
44Angola16557.02.8950.041.0242.0667.018.01.00.0242.02.42
55Antigua and Barbuda84.01.386.03.065.018.04.00.05.070.00.70
66Argentina39134.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
77Armenia3010.0-0.337390.0711.026665.010014.073.06.0187.026852.0268.52
89Austria8327.00.420558.0713.018246.01599.086.01.037.018283.0182.83
910Azerbaijan8406.00.630446.0423.023242.06781.0396.06.0558.023800.0238.00

Last rows

df_indexCountryPopulation_(in_thousands)_totalPopulation_annual_growth_rate_(%)ConfirmedDeathsRecoveredActiveNew_casesNew_deathsNew_recoveredTotal_RecoveredRecovered_Percent
152173Sweden9078.00.4NaNNaNNaNNaNNaNNaNNaNNaNNaN
153174Switzerland7455.00.41128.02.0986.0140.013.00.04.0990.09.90
154175Syria19408.02.767096.01636.037202.028258.0835.011.0317.037519.0375.19
155177Tajikistan6640.01.4301708.045844.01437.0254427.0688.07.03.01440.014.40
156178Tanzania39459.02.51202.035.0951.0216.010.01.03.0954.09.54
157180Timor-Leste1114.04.315988.0146.09959.05883.0525.04.0213.010172.0101.72
158181Togo6410.02.7431.00.0365.066.011.00.00.0365.03.65
159182Tonga100.00.510621.078.03752.06791.0152.02.00.03752.037.52
160183Trinidad and Tobago1328.00.410.01.08.01.00.00.00.08.00.08
161184Tunisia10215.01.11691.0483.0833.0375.010.04.036.0869.08.69